,
Rafael H. M. Pereira [aut, cre]
,
Daniel Herszenhut [aut] ,
Carlos Kaue Vieira Braga [aut]
,
Matthew Wigginton Bhagat-Conway [aut]
,
Luyu Liu [ctb] ,
Ipea - Institute for Applied Economic Research [cph, fnd]| Title: | Rapid Realistic Routing with 'R5' |
|---|---|
| Description: | Rapid realistic routing on multimodal transport networks (walk, bike, public transport and car) using 'R5', the Rapid Realistic Routing on Real-world and Reimagined networks engine <https://github.com/conveyal/r5>. The package allows users to generate detailed routing analysis or calculate travel time and monetary cost matrices using seamless parallel computing on top of the R5 Java machine. While R5 is developed by Conveyal, the package r5r is independently developed by a team at the Institute for Applied Economic Research (Ipea) with contributions from collaborators. Apart from the documentation in this package, users will find additional information on R5 documentation at <https://docs.conveyal.com/>. Although we try to keep new releases of r5r in synchrony with R5, the development of R5 follows Conveyal's independent update process. Hence, users should confirm the R5 version implied by the Conveyal user manual (see <https://docs.conveyal.com/changelog>) corresponds with the R5 version that r5r depends on. This version of r5r depends on R5 v7.1. |
| Authors: | Marcus Saraiva [aut] ,
Rafael H. M. Pereira [aut, cre]
,
Daniel Herszenhut [aut] ,
Carlos Kaue Vieira Braga [aut]
,
Matthew Wigginton Bhagat-Conway [aut]
,
Luyu Liu [ctb] ,
Ipea - Institute for Applied Economic Research [cph, fnd] |
| Maintainer: | Rafael H. M. Pereira <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 2.0.0 |
| Built: | 2024-12-13 03:08:32 UTC |
| Source: | https://github.com/ipeaGIT/r5r |
Fast computation of access to opportunities given a selected decay function.
accessibility( r5r_core, origins, destinations, opportunities_colnames = "opportunities", mode = "WALK", mode_egress = "WALK", departure_datetime = Sys.time(), time_window = 10L, percentiles = 50L, decay_function = "step", cutoffs = NULL, decay_value = NULL, fare_structure = NULL, max_fare = Inf, max_walk_time = Inf, max_bike_time = Inf, max_car_time = Inf, max_trip_duration = 120L, walk_speed = 3.6, bike_speed = 12, max_rides = 3, max_lts = 2, draws_per_minute = 5L, n_threads = Inf, verbose = FALSE, progress = FALSE, output_dir = NULL )accessibility( r5r_core, origins, destinations, opportunities_colnames = "opportunities", mode = "WALK", mode_egress = "WALK", departure_datetime = Sys.time(), time_window = 10L, percentiles = 50L, decay_function = "step", cutoffs = NULL, decay_value = NULL, fare_structure = NULL, max_fare = Inf, max_walk_time = Inf, max_bike_time = Inf, max_car_time = Inf, max_trip_duration = 120L, walk_speed = 3.6, bike_speed = 12, max_rides = 3, max_lts = 2, draws_per_minute = 5L, n_threads = Inf, verbose = FALSE, progress = FALSE, output_dir = NULL )
r5r_core |
An object to connect with the R5 routing engine, created with
|
origins, destinations
|
Either a |
opportunities_colnames |
A character vector. The names of the columns
in the |
mode |
A character vector. The transport modes allowed for access,
transfer and vehicle legs of the trips. Defaults to |
mode_egress |
A character vector. The transport mode used after egress
from the last public transport. It can be either |
departure_datetime |
A POSIXct object. Please note that the departure
time only influences public transport legs. When working with public
transport networks, please check the |
time_window |
An integer. The time window in minutes for which |
percentiles |
An integer vector (max length of 5). Specifies the
percentile to use when returning accessibility estimates within the given
time window. Please note that this parameter is applied to the travel time
estimates that generate the accessibility results, and not to the
accessibility distribution itself (i.e. if the 25th percentile is
specified, the accessibility is calculated from the 25th percentile travel
time, which may or may not be equal to the 25th percentile of the
accessibility distribution itself). Defaults to 50, returning the
accessibility calculated from the median travel time. If a vector with
length bigger than 1 is passed, the output contains an additional column
that specifies the percentile of each accessibility estimate. Due to
upstream restrictions, only 5 percentiles can be specified at a time. For
more details, please see |
decay_function |
A string. Which decay function to use when calculating
accessibility. One of |
cutoffs |
A numeric vector (maximum length of 12). This parameter has
different effects for each decay function: it indicates the cutoff times
in minutes when calculating cumulative opportunities accessibility with
the |
decay_value |
A number. Extra parameter to be passed to the selected
|
fare_structure |
A fare structure object, following the convention
set in |
max_fare |
A number. The maximum value that trips can cost when calculating the fastest journey between each origin and destination pair. |
max_walk_time |
An integer. The maximum walking time (in minutes) to
access and egress the transit network, to make transfers within the network
or to complete walk-only trips. Defaults to no restrictions (numeric value
of |
max_bike_time |
An integer. The maximum cycling time (in minutes) to
access and egress the transit network, to make transfers within the network
or to complete bicycle-only trips. Defaults to no restrictions (numeric
value of |
max_car_time |
An integer. The maximum driving time (in minutes) to
access and egress the transit network. Defaults to no restrictions, as long
as |
max_trip_duration |
An integer. The maximum trip duration in minutes. Defaults to 120 minutes (2 hours). |
walk_speed |
A numeric. Average walk speed in km/h. Defaults to 3.6 km/h. |
bike_speed |
A numeric. Average cycling speed in km/h. Defaults to 12 km/h. |
max_rides |
An integer. The maximum number of public transport rides allowed in the same trip. Defaults to 3. |
max_lts |
An integer between 1 and 4. The maximum level of traffic stress that cyclists will tolerate. A value of 1 means cyclists will only travel through the quietest streets, while a value of 4 indicates cyclists can travel through any road. Defaults to 2. Please see details for more information. |
draws_per_minute |
An integer. The number of Monte Carlo draws to
perform per time window minute when calculating travel time matrices and
when estimating accessibility. Defaults to 5. This would mean 300 draws in
a 60-minute time window, for example. This parameter only affects the
results when the GTFS feeds contain a |
n_threads |
An integer. The number of threads to use when running the router in parallel. Defaults to use all available threads (Inf). |
verbose |
A logical. Whether to show |
progress |
A logical. Whether to show a progress counter when running
the router. Defaults to |
output_dir |
Either |
A data.table with accessibility estimates for all origin points.
This data.table contain columns listing the origin id, the type of
opportunities to which accessibility was calculated, the travel time
percentile considered in the accessibility estimate and the specified
cutoff values (except in when decay_function is fixed_exponential, in
which case the cutoff parameter is not used). If output_dir is not
NULL, the function returns the path specified in that parameter, in
which the .csv files containing the results are saved.
R5 allows one to use different decay functions when calculating
accessibility. Please see the original R5 documentation from Conveyal for
more information on each one one
(https://docs.conveyal.com/learn-more/decay-functions). A summary of each
available option, as well as the value passed to decay_function to use it
(inside parentheses) are listed below:
Step, also known as cumulative opportunities ("step"):
a binary decay function used to find the sum of available opportunities
within a specific travel time cutoff.
Logistic CDF ("logistic"):
This is the logistic function, i.e. the cumulative distribution function of
the logistic distribution, expressed such that its parameters are the median
(inflection point) and standard deviation. This function applies a sigmoid
rolloff that has a convenient relationship to discrete choice theory. Its
parameters can be set to reflect a whole population's tolerance for making
trips with different travel times. The function's value represents the
probability that a randomly chosen member of the population would accept
making a trip, given its duration. Opportunities are then weighted by how
likely it is that a person would consider them "reachable".
Calibration: The median parameter is controlled by the cutoff
parameter, leaving only the standard deviation to configure through the
decay_value parameter.
Fixed Exponential ("fixed_exponential"):
This function is of the form exp(-Lt) where L is a single fixed decay
constant in the range (0, 1). It is constrained to be positive to ensure
weights decrease (rather than grow) with increasing travel time.
Calibration: This function is controlled exclusively by the L constant,
given by the decay_value parameter. Values provided in cutoffs are
ignored.
Half-life Exponential Decay ("exponential"):
This is similar to the fixed-exponential option above, but in this case the
decay parameter is inferred from the cutoffs parameter values, which is
treated as the half-life of the decay.
Linear ("linear"):
This is a simple, vaguely sigmoid option, which may be useful when you have
a sense of a maximum travel time that would be tolerated by any traveler,
and a minimum time below which all travel is perceived to be equally easy.
Calibration: The transition region is transposable and symmetric around
the cutoffs parameter values, taking decay_value minutes to taper down
from one to zero.
R5 allows for multiple combinations of transport modes. The options
include:
Transit modes: TRAM, SUBWAY, RAIL, BUS, FERRY, CABLE_CAR,
GONDOLA, FUNICULAR. The option TRANSIT automatically considers all
public transport modes available.
Non transit modes: WALK, BICYCLE, CAR, BICYCLE_RENT,
CAR_PARK.
When cycling is enabled in R5 (by passing the value BIKE to either
mode or mode_egress), setting max_lts will allow cycling only on
streets with a given level of danger/stress. Setting max_lts to 1, for
example, will allow cycling only on separated bicycle infrastructure or
low-traffic streets and routing will revert to walking when traversing any
links with LTS exceeding 1. Setting max_lts to 3 will allow cycling on
links with LTS 1, 2 or 3. Routing also reverts to walking if the street
segment is tagged as non-bikable in OSM (e.g. a staircase), independently of
the specified max LTS.
The default methodology for assigning LTS values to network edges is based on commonly tagged attributes of OSM ways. See more info about LTS in the original documentation of R5 from Conveyal at https://docs.conveyal.com/learn-more/traffic-stress. In summary:
LTS 1: Tolerable for children. This includes low-speed, low-volume streets, as well as those with separated bicycle facilities (such as parking-protected lanes or cycle tracks).
LTS 2: Tolerable for the mainstream adult population. This includes streets where cyclists have dedicated lanes and only have to interact with traffic at formal crossing.
LTS 3: Tolerable for "enthused and confident" cyclists. This includes streets which may involve close proximity to moderate- or high-speed vehicular traffic.
LTS 4: Tolerable only for "strong and fearless" cyclists. This includes streets where cyclists are required to mix with moderate- to high-speed vehicular traffic.
For advanced users, you can provide custom LTS values by adding a tag <key = "lts"> to the osm.pbf file.
r5r ignores the timezone attribute of datetime objects when parsing dates
and times, using the study area's timezone instead. For example, let's say
you are running some calculations using Rio de Janeiro, Brazil, as your study
area. The datetime as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S") will be parsed as May 13th, 2019, 14:00h in
Rio's local time, as expected. But as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S", tz = "Europe/Paris") will also be parsed as
the exact same date and time in Rio's local time, perhaps surprisingly,
ignoring the timezone attribute.
The travel_time_matrix(), expanded_travel_time_matrix() and
accessibility() functions use an R5-specific extension to the RAPTOR
routing algorithm (see Conway et al., 2017). This RAPTOR extension uses a
systematic sample of one departure per minute over the time window set by the
user in the 'time_window' parameter. A detailed description of base RAPTOR
can be found in Delling et al (2015). However, whenever the user includes
transit fares inputs to these functions, they automatically switch to use an
R5-specific extension to the McRAPTOR routing algorithm.
Conway, M. W., Byrd, A., & van der Linden, M. (2017). Evidence-based transit and land use sketch planning using interactive accessibility methods on combined schedule and headway-based networks. Transportation Research Record, 2653(1), 45-53. doi:10.3141/2653-06
Delling, D., Pajor, T., & Werneck, R. F. (2015). Round-based public transit routing. Transportation Science, 49(3), 591-604. doi:10.1287/trsc.2014.0534
library(r5r) data_path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(data_path) points <- read.csv(file.path(data_path, "poa_hexgrid.csv"))[1:5, ] departure_datetime <- as.POSIXct( "13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S" ) access <- accessibility( r5r_core, origins = points, destinations = points, opportunities_colnames = "schools", mode = "WALK", departure_datetime = departure_datetime, decay_function = "step", cutoffs = 30, max_trip_duration = 30 ) head(access) # using a different decay function access <- accessibility( r5r_core, origins = points, destinations = points, opportunities_colnames = "schools", mode = "WALK", departure_datetime = departure_datetime, decay_function = "logistic", cutoffs = 30, decay_value = 1, max_trip_duration = 30 ) head(access) # using several cutoff values access <- accessibility( r5r_core, origins = points, destinations = points, opportunities_colnames = "schools", mode = "WALK", departure_datetime = departure_datetime, decay_function = "step", cutoffs = c(25, 30), max_trip_duration = 30 ) head(access) # calculating access to different types of opportunities access <- accessibility( r5r_core, origins = points, destinations = points, opportunities_colnames = c("schools", "healthcare"), mode = "WALK", departure_datetime = departure_datetime, decay_function = "step", cutoffs = 30, max_trip_duration = 30 ) head(access) stop_r5(r5r_core)library(r5r) data_path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(data_path) points <- read.csv(file.path(data_path, "poa_hexgrid.csv"))[1:5, ] departure_datetime <- as.POSIXct( "13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S" ) access <- accessibility( r5r_core, origins = points, destinations = points, opportunities_colnames = "schools", mode = "WALK", departure_datetime = departure_datetime, decay_function = "step", cutoffs = 30, max_trip_duration = 30 ) head(access) # using a different decay function access <- accessibility( r5r_core, origins = points, destinations = points, opportunities_colnames = "schools", mode = "WALK", departure_datetime = departure_datetime, decay_function = "logistic", cutoffs = 30, decay_value = 1, max_trip_duration = 30 ) head(access) # using several cutoff values access <- accessibility( r5r_core, origins = points, destinations = points, opportunities_colnames = "schools", mode = "WALK", departure_datetime = departure_datetime, decay_function = "step", cutoffs = c(25, 30), max_trip_duration = 30 ) head(access) # calculating access to different types of opportunities access <- accessibility( r5r_core, origins = points, destinations = points, opportunities_colnames = c("schools", "healthcare"), mode = "WALK", departure_datetime = departure_datetime, decay_function = "step", cutoffs = 30, max_trip_duration = 30 ) head(access) stop_r5(r5r_core)
Returns detailed trip information between origin-destination pairs. The output includes the waiting and moving time in each trip leg, as well as some info such as the distance traveled, the routes used and the geometry of each leg. Please note that this function was originally conceptualized as a trip planning functionality, similar to other commercial and non-commercial APIs and apps (e.g. Moovit, Google's Directions API, OpenTripPlanning's PlannerResource API). Thus, it consumes much more time and memory than the other (more analytical) routing functions included in the package.
detailed_itineraries( r5r_core, origins, destinations, mode = "WALK", mode_egress = "WALK", departure_datetime = Sys.time(), time_window = 10L, suboptimal_minutes = 0L, fare_structure = NULL, max_fare = Inf, max_walk_time = Inf, max_bike_time = Inf, max_car_time = Inf, max_trip_duration = 120L, walk_speed = 3.6, bike_speed = 12, max_rides = 3, max_lts = 2, shortest_path = TRUE, all_to_all = FALSE, n_threads = Inf, verbose = FALSE, progress = FALSE, drop_geometry = FALSE, output_dir = NULL )detailed_itineraries( r5r_core, origins, destinations, mode = "WALK", mode_egress = "WALK", departure_datetime = Sys.time(), time_window = 10L, suboptimal_minutes = 0L, fare_structure = NULL, max_fare = Inf, max_walk_time = Inf, max_bike_time = Inf, max_car_time = Inf, max_trip_duration = 120L, walk_speed = 3.6, bike_speed = 12, max_rides = 3, max_lts = 2, shortest_path = TRUE, all_to_all = FALSE, n_threads = Inf, verbose = FALSE, progress = FALSE, drop_geometry = FALSE, output_dir = NULL )
r5r_core |
An object to connect with the R5 routing engine, created with
|
origins, destinations
|
Either a |
mode |
A character vector. The transport modes allowed for access,
transfer and vehicle legs of the trips. Defaults to |
mode_egress |
A character vector. The transport mode used after egress
from the last public transport. It can be either |
departure_datetime |
A POSIXct object. Please note that the departure
time only influences public transport legs. When working with public
transport networks, please check the |
time_window |
An integer. The time window in minutes for which |
suboptimal_minutes |
A number. The difference in minutes that each
non-optimal RAPTOR branch can have from the optimal branch without being
disregarded by the routing algorithm. If, for example, users set
|
fare_structure |
A fare structure object, following the convention
set in |
max_fare |
A number. The maximum value that trips can cost when calculating the fastest journey between each origin and destination pair. |
max_walk_time |
An integer. The maximum walking time (in minutes) to
access and egress the transit network, to make transfers within the network
or to complete walk-only trips. Defaults to no restrictions (numeric value
of |
max_bike_time |
An integer. The maximum cycling time (in minutes) to
access and egress the transit network, to make transfers within the network
or to complete bicycle-only trips. Defaults to no restrictions (numeric
value of |
max_car_time |
An integer. The maximum driving time (in minutes) to
access and egress the transit network. Defaults to no restrictions, as long
as |
max_trip_duration |
An integer. The maximum trip duration in minutes. Defaults to 120 minutes (2 hours). |
walk_speed |
A numeric. Average walk speed in km/h. Defaults to 3.6 km/h. |
bike_speed |
A numeric. Average cycling speed in km/h. Defaults to 12 km/h. |
max_rides |
An integer. The maximum number of public transport rides allowed in the same trip. Defaults to 3. |
max_lts |
An integer between 1 and 4. The maximum level of traffic stress that cyclists will tolerate. A value of 1 means cyclists will only travel through the quietest streets, while a value of 4 indicates cyclists can travel through any road. Defaults to 2. Please see details for more information. |
shortest_path |
A logical. Whether the function should only return the fastest itinerary between each origin and destination pair (the default) or multiple alternatives. |
all_to_all |
A logical. Whether to query routes between the 1st origin
to the 1st destination, then the 2nd origin to the 2nd destination, and so
on ( |
n_threads |
An integer. The number of threads to use when running the router in parallel. Defaults to use all available threads (Inf). |
verbose |
A logical. Whether to show |
progress |
A logical. Whether to show a progress counter when running
the router. Defaults to |
drop_geometry |
A logical. Whether the output should include the
geometry of each trip leg or not. The default value of |
output_dir |
Either |
When drop_geometry is FALSE, the function outputs a LINESTRING sf with detailed information on the itineraries between the specified
origins and destinations. When TRUE, the output is a data.table. All
distances are in meters and travel times are in minutes. If output_dir
is not NULL, the function returns the path specified in that parameter,
in which the .csv files containing the results are saved.
R5 allows for multiple combinations of transport modes. The options
include:
Transit modes: TRAM, SUBWAY, RAIL, BUS, FERRY, CABLE_CAR,
GONDOLA, FUNICULAR. The option TRANSIT automatically considers all
public transport modes available.
Non transit modes: WALK, BICYCLE, CAR, BICYCLE_RENT,
CAR_PARK.
When cycling is enabled in R5 (by passing the value BIKE to either
mode or mode_egress), setting max_lts will allow cycling only on
streets with a given level of danger/stress. Setting max_lts to 1, for
example, will allow cycling only on separated bicycle infrastructure or
low-traffic streets and routing will revert to walking when traversing any
links with LTS exceeding 1. Setting max_lts to 3 will allow cycling on
links with LTS 1, 2 or 3. Routing also reverts to walking if the street
segment is tagged as non-bikable in OSM (e.g. a staircase), independently of
the specified max LTS.
The default methodology for assigning LTS values to network edges is based on commonly tagged attributes of OSM ways. See more info about LTS in the original documentation of R5 from Conveyal at https://docs.conveyal.com/learn-more/traffic-stress. In summary:
LTS 1: Tolerable for children. This includes low-speed, low-volume streets, as well as those with separated bicycle facilities (such as parking-protected lanes or cycle tracks).
LTS 2: Tolerable for the mainstream adult population. This includes streets where cyclists have dedicated lanes and only have to interact with traffic at formal crossing.
LTS 3: Tolerable for "enthused and confident" cyclists. This includes streets which may involve close proximity to moderate- or high-speed vehicular traffic.
LTS 4: Tolerable only for "strong and fearless" cyclists. This includes streets where cyclists are required to mix with moderate- to high-speed vehicular traffic.
For advanced users, you can provide custom LTS values by adding a tag <key = "lts"> to the osm.pbf file.
r5r ignores the timezone attribute of datetime objects when parsing dates
and times, using the study area's timezone instead. For example, let's say
you are running some calculations using Rio de Janeiro, Brazil, as your study
area. The datetime as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S") will be parsed as May 13th, 2019, 14:00h in
Rio's local time, as expected. But as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S", tz = "Europe/Paris") will also be parsed as
the exact same date and time in Rio's local time, perhaps surprisingly,
ignoring the timezone attribute.
The detailed_itineraries() and pareto_frontier() functions use an
R5-specific extension to the McRAPTOR routing algorithm. The
implementation used in detailed_itineraries() allows the router to find
paths that are optimal and less than optimal in terms of travel time, with
some heuristics around multiple access modes, riding the same patterns, etc.
The specific extension to McRAPTOR to do suboptimal path routing is not
documented yet, but a detailed description of base McRAPTOR can be found in
Delling et al (2015). The implementation used in pareto_frontier(), on the
other hand, returns only the fastest trip within a given monetary cutoff,
ignoring slower trips that cost the same. A detailed discussion on the
algorithm can be found in Conway and Stewart (2019).
Delling, D., Pajor, T., & Werneck, R. F. (2015). Round-based public transit routing. Transportation Science, 49(3), 591-604. doi:10.1287/trsc.2014.0534
Conway, M. W., & Stewart, A. F. (2019). Getting Charlie off the MTA: a multiobjective optimization method to account for cost constraints in public transit accessibility metrics. International Journal of Geographical Information Science, 33(9), 1759-1787. doi:10.1080/13658816.2019.1605075
Other routing:
expanded_travel_time_matrix(),
pareto_frontier(),
travel_time_matrix()
library(r5r) # build transport network data_path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(data_path) # load origin/destination points points <- read.csv(file.path(data_path, "poa_points_of_interest.csv")) # inputs departure_datetime <- as.POSIXct( "13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S" ) det <- detailed_itineraries( r5r_core, origins = points[10,], destinations = points[12,], mode = c("WALK", "TRANSIT"), departure_datetime = departure_datetime, max_trip_duration = 60 ) head(det) stop_r5(r5r_core)library(r5r) # build transport network data_path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(data_path) # load origin/destination points points <- read.csv(file.path(data_path, "poa_points_of_interest.csv")) # inputs departure_datetime <- as.POSIXct( "13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S" ) det <- detailed_itineraries( r5r_core, origins = points[10,], destinations = points[12,], mode = c("WALK", "TRANSIT"), departure_datetime = departure_datetime, max_trip_duration = 60 ) head(det) stop_r5(r5r_core)
R5.jar
Downloads R5.jar and saves it locally, inside the package directory.
download_r5( version = NULL, quiet = FALSE, force_update = FALSE, temp_dir = FALSE )download_r5( version = NULL, quiet = FALSE, force_update = FALSE, temp_dir = FALSE )
version |
A string. The version of R5 to be downloaded. When |
quiet |
A logical. Whether to show informative messages when downloading
the file. Defaults to |
force_update |
A logical. Whether to overwrite a previously downloaded
|
temp_dir |
A logical. Whether the file should be saved in a temporary
directory. Defaults to |
The path to the downloaded file.
Other setup:
setup_r5()
library(r5r) download_r5(temp_dir = TRUE)library(r5r) download_r5(temp_dir = TRUE)
Detailed computation of travel time estimates between one or multiple origin destination pairs. Results show the travel time of the fastest route alternative departing each minute within a specified time window. Please note this function can be very memory intensive for large data sets and time windows.
expanded_travel_time_matrix( r5r_core, origins, destinations, mode = "WALK", mode_egress = "WALK", departure_datetime = Sys.time(), time_window = 10L, breakdown = FALSE, max_walk_time = Inf, max_bike_time = Inf, max_car_time = Inf, max_trip_duration = 120L, walk_speed = 3.6, bike_speed = 12, max_rides = 3, max_lts = 2, draws_per_minute = 5L, n_threads = Inf, verbose = FALSE, progress = FALSE, output_dir = NULL )expanded_travel_time_matrix( r5r_core, origins, destinations, mode = "WALK", mode_egress = "WALK", departure_datetime = Sys.time(), time_window = 10L, breakdown = FALSE, max_walk_time = Inf, max_bike_time = Inf, max_car_time = Inf, max_trip_duration = 120L, walk_speed = 3.6, bike_speed = 12, max_rides = 3, max_lts = 2, draws_per_minute = 5L, n_threads = Inf, verbose = FALSE, progress = FALSE, output_dir = NULL )
r5r_core |
An object to connect with the R5 routing engine, created with
|
origins, destinations
|
Either a |
mode |
A character vector. The transport modes allowed for access,
transfer and vehicle legs of the trips. Defaults to |
mode_egress |
A character vector. The transport mode used after egress
from the last public transport. It can be either |
departure_datetime |
A POSIXct object. Please note that the departure
time only influences public transport legs. When working with public
transport networks, please check the |
time_window |
An integer. The time window in minutes for which |
breakdown |
A logical. Whether to include detailed information about
each trip in the output. If |
max_walk_time |
An integer. The maximum walking time (in minutes) to
access and egress the transit network, to make transfers within the network
or to complete walk-only trips. Defaults to no restrictions (numeric value
of |
max_bike_time |
An integer. The maximum cycling time (in minutes) to
access and egress the transit network, to make transfers within the network
or to complete bicycle-only trips. Defaults to no restrictions (numeric
value of |
max_car_time |
An integer. The maximum driving time (in minutes) to
access and egress the transit network. Defaults to no restrictions, as long
as |
max_trip_duration |
An integer. The maximum trip duration in minutes. Defaults to 120 minutes (2 hours). |
walk_speed |
A numeric. Average walk speed in km/h. Defaults to 3.6 km/h. |
bike_speed |
A numeric. Average cycling speed in km/h. Defaults to 12 km/h. |
max_rides |
An integer. The maximum number of public transport rides allowed in the same trip. Defaults to 3. |
max_lts |
An integer between 1 and 4. The maximum level of traffic stress that cyclists will tolerate. A value of 1 means cyclists will only travel through the quietest streets, while a value of 4 indicates cyclists can travel through any road. Defaults to 2. Please see details for more information. |
draws_per_minute |
An integer. The number of Monte Carlo draws to
perform per time window minute when calculating travel time matrices and
when estimating accessibility. Defaults to 5. This would mean 300 draws in
a 60-minute time window, for example. This parameter only affects the
results when the GTFS feeds contain a |
n_threads |
An integer. The number of threads to use when running the router in parallel. Defaults to use all available threads (Inf). |
verbose |
A logical. Whether to show |
progress |
A logical. Whether to show a progress counter when running
the router. Defaults to |
output_dir |
Either |
A data.table with travel time estimates (in minutes) and the
routes used in each trip between origin and destination pairs, for each
minute of the specified time window. Each set of origin, destination and
departure minute can appear up to N times, where N is the number of Monte
Carlo draws specified in the function arguments (please note that this
only applies when the GTFS feeds that describe the transit network include
a frequencies table, otherwise only a single draw is performed). A pair is
completely absent from the final output if no trips could be completed in
any of the minutes of the time window. If for a single pair trips could be
completed in some of the minutes of the time window, but not for all of
them, the minutes in which trips couldn't be completed will have NA
travel time and routes used. If output_dir is not NULL, the function
returns the path specified in that parameter, in which the .csv files
containing the results are saved.
R5 allows for multiple combinations of transport modes. The options
include:
Transit modes: TRAM, SUBWAY, RAIL, BUS, FERRY, CABLE_CAR,
GONDOLA, FUNICULAR. The option TRANSIT automatically considers all
public transport modes available.
Non transit modes: WALK, BICYCLE, CAR, BICYCLE_RENT,
CAR_PARK.
When cycling is enabled in R5 (by passing the value BIKE to either
mode or mode_egress), setting max_lts will allow cycling only on
streets with a given level of danger/stress. Setting max_lts to 1, for
example, will allow cycling only on separated bicycle infrastructure or
low-traffic streets and routing will revert to walking when traversing any
links with LTS exceeding 1. Setting max_lts to 3 will allow cycling on
links with LTS 1, 2 or 3. Routing also reverts to walking if the street
segment is tagged as non-bikable in OSM (e.g. a staircase), independently of
the specified max LTS.
The default methodology for assigning LTS values to network edges is based on commonly tagged attributes of OSM ways. See more info about LTS in the original documentation of R5 from Conveyal at https://docs.conveyal.com/learn-more/traffic-stress. In summary:
LTS 1: Tolerable for children. This includes low-speed, low-volume streets, as well as those with separated bicycle facilities (such as parking-protected lanes or cycle tracks).
LTS 2: Tolerable for the mainstream adult population. This includes streets where cyclists have dedicated lanes and only have to interact with traffic at formal crossing.
LTS 3: Tolerable for "enthused and confident" cyclists. This includes streets which may involve close proximity to moderate- or high-speed vehicular traffic.
LTS 4: Tolerable only for "strong and fearless" cyclists. This includes streets where cyclists are required to mix with moderate- to high-speed vehicular traffic.
For advanced users, you can provide custom LTS values by adding a tag <key = "lts"> to the osm.pbf file.
r5r ignores the timezone attribute of datetime objects when parsing dates
and times, using the study area's timezone instead. For example, let's say
you are running some calculations using Rio de Janeiro, Brazil, as your study
area. The datetime as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S") will be parsed as May 13th, 2019, 14:00h in
Rio's local time, as expected. But as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S", tz = "Europe/Paris") will also be parsed as
the exact same date and time in Rio's local time, perhaps surprisingly,
ignoring the timezone attribute.
The travel_time_matrix(), expanded_travel_time_matrix() and
accessibility() functions use an R5-specific extension to the RAPTOR
routing algorithm (see Conway et al., 2017). This RAPTOR extension uses a
systematic sample of one departure per minute over the time window set by the
user in the 'time_window' parameter. A detailed description of base RAPTOR
can be found in Delling et al (2015). However, whenever the user includes
transit fares inputs to these functions, they automatically switch to use an
R5-specific extension to the McRAPTOR routing algorithm.
Conway, M. W., Byrd, A., & van der Linden, M. (2017). Evidence-based transit and land use sketch planning using interactive accessibility methods on combined schedule and headway-based networks. Transportation Research Record, 2653(1), 45-53. doi:10.3141/2653-06
Delling, D., Pajor, T., & Werneck, R. F. (2015). Round-based public transit routing. Transportation Science, 49(3), 591-604. doi:10.1287/trsc.2014.0534
Other routing:
detailed_itineraries(),
pareto_frontier(),
travel_time_matrix()
library(r5r) # build transport network data_path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(data_path) # load origin/destination points points <- read.csv(file.path(data_path, "poa_points_of_interest.csv")) departure_datetime <- as.POSIXct( "13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S" ) # by default only returns the total time between each pair in each minute of # the specified time window ettm <- expanded_travel_time_matrix( r5r_core, origins = points, destinations = points, mode = c("WALK", "TRANSIT"), time_window = 20, departure_datetime = departure_datetime, max_trip_duration = 60 ) head(ettm) # when breakdown = TRUE the output contains much more information ettm <- expanded_travel_time_matrix( r5r_core, origins = points, destinations = points, mode = c("WALK", "TRANSIT"), time_window = 20, departure_datetime = departure_datetime, max_trip_duration = 60, breakdown = TRUE ) head(ettm) stop_r5(r5r_core)library(r5r) # build transport network data_path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(data_path) # load origin/destination points points <- read.csv(file.path(data_path, "poa_points_of_interest.csv")) departure_datetime <- as.POSIXct( "13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S" ) # by default only returns the total time between each pair in each minute of # the specified time window ettm <- expanded_travel_time_matrix( r5r_core, origins = points, destinations = points, mode = c("WALK", "TRANSIT"), time_window = 20, departure_datetime = departure_datetime, max_trip_duration = 60 ) head(ettm) # when breakdown = TRUE the output contains much more information ettm <- expanded_travel_time_matrix( r5r_core, origins = points, destinations = points, mode = c("WALK", "TRANSIT"), time_window = 20, departure_datetime = departure_datetime, max_trip_duration = 60, breakdown = TRUE ) head(ettm) stop_r5(r5r_core)
Finds the snapped location of points on R5 network. Snapping is a very
important step of the routing process, which is when the origins and
destinations specified by the user are actually positioned on the network
created by R5. The snapping process in R5 is composed of two rounds.
First, it tries to snap the points within a radius of 300 meters from
themselves. If the first round is unsuccessful, then R5 expands the search
radius to 1.6 km. If yet again it is unsuccessful, then the unsnapped points
won't be used during the routing process. The snapped location of each point
depends on the transport mode set by the user, because some network edges
are not available to specific modes (e.g. a pedestrian-only street cannot be
used to snap car trips).
find_snap(r5r_core, points, mode = "WALK")find_snap(r5r_core, points, mode = "WALK")
r5r_core |
An object to connect with the R5 routing engine, created with
|
points |
Either a |
mode |
A string. Which mode to consider when trying to snap the points
to the network. Defaults to |
A data.table with the original points, their respective
snapped coordinates on the street network and the Euclidean distance ( in
meters) between the original points and their snapped location. Points that
could not be snapped show NA coordinates and found = FALSE.
Other network functions:
street_network_to_sf(),
transit_network_to_sf()
library(r5r) path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(data_path = path) points <- read.csv(file.path(path, "poa_hexgrid.csv")) snap_df <- find_snap(r5r_core, points, mode = "WALK") stop_r5(r5r_core)library(r5r) path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(data_path = path) points <- read.csv(file.path(path, "poa_hexgrid.csv")) snap_df <- find_snap(r5r_core, points, mode = "WALK") stop_r5(r5r_core)
Fast computation of isochrones from a given location. The function can return either polygon-based or line-based isochrones. Polygon-based isochrones are generated as concave polygons based on the travel times from the trip origin to all nodes in the transport network. Meanwhile, line-based isochronesare based on travel times from each origin to the centroids of all segments in the transport network.
isochrone( r5r_core, origins, mode = "transit", mode_egress = "walk", cutoffs = c(0, 15, 30), sample_size = 0.8, departure_datetime = Sys.time(), polygon_output = TRUE, time_window = 10L, max_walk_time = Inf, max_bike_time = Inf, max_car_time = Inf, max_trip_duration = 120L, walk_speed = 3.6, bike_speed = 12, max_rides = 3, max_lts = 2, draws_per_minute = 5L, n_threads = Inf, verbose = FALSE, progress = TRUE )isochrone( r5r_core, origins, mode = "transit", mode_egress = "walk", cutoffs = c(0, 15, 30), sample_size = 0.8, departure_datetime = Sys.time(), polygon_output = TRUE, time_window = 10L, max_walk_time = Inf, max_bike_time = Inf, max_car_time = Inf, max_trip_duration = 120L, walk_speed = 3.6, bike_speed = 12, max_rides = 3, max_lts = 2, draws_per_minute = 5L, n_threads = Inf, verbose = FALSE, progress = TRUE )
r5r_core |
An object to connect with the R5 routing engine, created with
|
origins |
Either a |
mode |
A character vector. The transport modes allowed for access,
transfer and vehicle legs of the trips. Defaults to |
mode_egress |
A character vector. The transport mode used after egress
from the last public transport. It can be either |
cutoffs |
numeric vector. Number of minutes to define the time span of
each Isochrone. Defaults to |
sample_size |
numeric. Sample size of nodes in the transport network used
to estimate isochrones. Defaults to |
departure_datetime |
A POSIXct object. Please note that the departure
time only influences public transport legs. When working with public
transport networks, please check the |
polygon_output |
A Logical. If |
time_window |
An integer. The time window in minutes for which |
max_walk_time |
An integer. The maximum walking time (in minutes) to
access and egress the transit network, or to make transfers within the
network. Defaults to no restrictions, as long as |
max_bike_time |
An integer. The maximum cycling time (in minutes) to
access and egress the transit network. Defaults to no restrictions, as
long as |
max_car_time |
An integer. The maximum driving time (in minutes) to
access and egress the transit network. Defaults to no restrictions, as
long as |
max_trip_duration |
An integer. The maximum trip duration in minutes. Defaults to 120 minutes (2 hours). |
walk_speed |
A numeric. Average walk speed in km/h. Defaults to 3.6 km/h. |
bike_speed |
A numeric. Average cycling speed in km/h. Defaults to 12 km/h. |
max_rides |
An integer. The maximum number of public transport rides allowed in the same trip. Defaults to 3. |
max_lts |
An integer between 1 and 4. The maximum level of traffic stress that cyclists will tolerate. A value of 1 means cyclists will only travel through the quietest streets, while a value of 4 indicates cyclists can travel through any road. Defaults to 2. Please see details for more information. |
draws_per_minute |
An integer. The number of Monte Carlo draws to
perform per time window minute when calculating travel time matrices and
when estimating accessibility. Defaults to 5. This would mean 300 draws in
a 60-minute time window, for example. This parameter only affects the
results when the GTFS feeds contain a |
n_threads |
An integer. The number of threads to use when running the
router in parallel. Defaults to use all available threads ( |
verbose |
A logical. Whether to show |
progress |
A logical. Whether to show a progress counter when running
the router. Defaults to |
A POLYGON "sf" "data.frame" for each isochrone of each origin.
R5 allows for multiple combinations of transport modes. The options
include:
Transit modes: TRAM, SUBWAY, RAIL, BUS, FERRY, CABLE_CAR,
GONDOLA, FUNICULAR. The option TRANSIT automatically considers all
public transport modes available.
Non transit modes: WALK, BICYCLE, CAR, BICYCLE_RENT,
CAR_PARK.
When cycling is enabled in R5 (by passing the value BIKE to either
mode or mode_egress), setting max_lts will allow cycling only on
streets with a given level of danger/stress. Setting max_lts to 1, for
example, will allow cycling only on separated bicycle infrastructure or
low-traffic streets and routing will revert to walking when traversing any
links with LTS exceeding 1. Setting max_lts to 3 will allow cycling on
links with LTS 1, 2 or 3. Routing also reverts to walking if the street
segment is tagged as non-bikable in OSM (e.g. a staircase), independently of
the specified max LTS.
The default methodology for assigning LTS values to network edges is based on commonly tagged attributes of OSM ways. See more info about LTS in the original documentation of R5 from Conveyal at https://docs.conveyal.com/learn-more/traffic-stress. In summary:
LTS 1: Tolerable for children. This includes low-speed, low-volume streets, as well as those with separated bicycle facilities (such as parking-protected lanes or cycle tracks).
LTS 2: Tolerable for the mainstream adult population. This includes streets where cyclists have dedicated lanes and only have to interact with traffic at formal crossing.
LTS 3: Tolerable for "enthused and confident" cyclists. This includes streets which may involve close proximity to moderate- or high-speed vehicular traffic.
LTS 4: Tolerable only for "strong and fearless" cyclists. This includes streets where cyclists are required to mix with moderate- to high-speed vehicular traffic.
For advanced users, you can provide custom LTS values by adding a tag <key = "lts"> to the osm.pbf file.
r5r ignores the timezone attribute of datetime objects when parsing dates
and times, using the study area's timezone instead. For example, let's say
you are running some calculations using Rio de Janeiro, Brazil, as your study
area. The datetime as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S") will be parsed as May 13th, 2019, 14:00h in
Rio's local time, as expected. But as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S", tz = "Europe/Paris") will also be parsed as
the exact same date and time in Rio's local time, perhaps surprisingly,
ignoring the timezone attribute.
The travel_time_matrix(), expanded_travel_time_matrix() and
accessibility() functions use an R5-specific extension to the RAPTOR
routing algorithm (see Conway et al., 2017). This RAPTOR extension uses a
systematic sample of one departure per minute over the time window set by the
user in the 'time_window' parameter. A detailed description of base RAPTOR
can be found in Delling et al (2015). However, whenever the user includes
transit fares inputs to these functions, they automatically switch to use an
R5-specific extension to the McRAPTOR routing algorithm.
Conway, M. W., Byrd, A., & van der Linden, M. (2017). Evidence-based transit and land use sketch planning using interactive accessibility methods on combined schedule and headway-based networks. Transportation Research Record, 2653(1), 45-53. doi:10.3141/2653-06
Delling, D., Pajor, T., & Werneck, R. F. (2015). Round-based public transit routing. Transportation Science, 49(3), 591-604. doi:10.1287/trsc.2014.0534
options(java.parameters = "-Xmx2G") library(r5r) library(ggplot2) # build transport network data_path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(data_path = data_path) # load origin/point of interest points <- read.csv(file.path(data_path, "poa_hexgrid.csv")) origin_1 <- points[936,] departure_datetime <- as.POSIXct( "13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S" ) # estimate polygon-based isochrone from origin_1 iso_poly <- isochrone(r5r_core, origins = origin_1, mode = "walk", polygon_output = TRUE, departure_datetime = departure_datetime, cutoffs = seq(0, 100, 10) ) head(iso_poly) # estimate line-based isochrone from origin_1 iso_lines <- isochrone(r5r_core, origins = origin_1, mode = "walk", polygon_output = FALSE, departure_datetime = departure_datetime, cutoffs = seq(0, 100, 10) ) head(iso_lines) # plot colors colors <- c('#ffe0a5','#ffcb69','#ffa600','#ff7c43','#f95d6a', '#d45087','#a05195','#665191','#2f4b7c','#003f5c') # polygons ggplot() + geom_sf(data=iso_poly, aes(fill=factor(isochrone))) + scale_fill_manual(values = colors) + theme_minimal() # lines ggplot() + geom_sf(data=iso_lines, aes(color=factor(isochrone))) + scale_color_manual(values = colors) + theme_minimal() stop_r5(r5r_core)options(java.parameters = "-Xmx2G") library(r5r) library(ggplot2) # build transport network data_path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(data_path = data_path) # load origin/point of interest points <- read.csv(file.path(data_path, "poa_hexgrid.csv")) origin_1 <- points[936,] departure_datetime <- as.POSIXct( "13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S" ) # estimate polygon-based isochrone from origin_1 iso_poly <- isochrone(r5r_core, origins = origin_1, mode = "walk", polygon_output = TRUE, departure_datetime = departure_datetime, cutoffs = seq(0, 100, 10) ) head(iso_poly) # estimate line-based isochrone from origin_1 iso_lines <- isochrone(r5r_core, origins = origin_1, mode = "walk", polygon_output = FALSE, departure_datetime = departure_datetime, cutoffs = seq(0, 100, 10) ) head(iso_lines) # plot colors colors <- c('#ffe0a5','#ffcb69','#ffa600','#ff7c43','#f95d6a', '#d45087','#a05195','#665191','#2f4b7c','#003f5c') # polygons ggplot() + geom_sf(data=iso_poly, aes(fill=factor(isochrone))) + scale_fill_manual(values = colors) + theme_minimal() # lines ggplot() + geom_sf(data=iso_lines, aes(color=factor(isochrone))) + scale_color_manual(values = colors) + theme_minimal() stop_r5(r5r_core)
Fast computation of travel time and monetary cost Pareto frontier between origin and destination pairs.
pareto_frontier( r5r_core, origins, destinations, mode = c("WALK", "TRANSIT"), mode_egress = "WALK", departure_datetime = Sys.time(), time_window = 10L, percentiles = 50L, max_walk_time = Inf, max_bike_time = Inf, max_car_time = Inf, max_trip_duration = 120L, fare_structure = NULL, fare_cutoffs = -1L, walk_speed = 3.6, bike_speed = 12, max_rides = 3, max_lts = 2, n_threads = Inf, verbose = FALSE, progress = FALSE, output_dir = NULL )pareto_frontier( r5r_core, origins, destinations, mode = c("WALK", "TRANSIT"), mode_egress = "WALK", departure_datetime = Sys.time(), time_window = 10L, percentiles = 50L, max_walk_time = Inf, max_bike_time = Inf, max_car_time = Inf, max_trip_duration = 120L, fare_structure = NULL, fare_cutoffs = -1L, walk_speed = 3.6, bike_speed = 12, max_rides = 3, max_lts = 2, n_threads = Inf, verbose = FALSE, progress = FALSE, output_dir = NULL )
r5r_core |
An object to connect with the R5 routing engine, created with
|
origins, destinations
|
Either a |
mode |
A character vector. The transport modes allowed for access,
transfer and vehicle legs of the trips. Defaults to |
mode_egress |
A character vector. The transport mode used after egress
from the last public transport. It can be either |
departure_datetime |
A POSIXct object. Please note that the departure
time only influences public transport legs. When working with public
transport networks, please check the |
time_window |
An integer. The time window in minutes for which |
percentiles |
An integer vector (max length of 5). Specifies the percentile to use when returning travel time estimates within the given time window. Please note that this parameter is applied to the travel time estimates only (e.g. if the 25th percentile is specified, and the output between A and B is 15 minutes and 10 dollars, 25% of all trips cheaper than 10 dollars taken between these points are shorter than 15 minutes). Defaults to 50, returning the median travel time. If a vector with length bigger than 1 is passed, the output contains an additional column that specifies the percentile of each travel time and monetary cost combination. Due to upstream restrictions, only 5 percentiles can be specified at a time. For more details, please see R5 documentation at https://docs.conveyal.com/analysis/methodology#accounting-for-variability. |
max_walk_time |
An integer. The maximum walking time (in minutes) to
access and egress the transit network, to make transfers within the network
or to complete walk-only trips. Defaults to no restrictions (numeric value
of |
max_bike_time |
An integer. The maximum cycling time (in minutes) to
access and egress the transit network, to make transfers within the network
or to complete bicycle-only trips. Defaults to no restrictions (numeric
value of |
max_car_time |
An integer. The maximum driving time (in minutes) to
access and egress the transit network. Defaults to no restrictions, as long
as |
max_trip_duration |
An integer. The maximum trip duration in minutes. Defaults to 120 minutes (2 hours). |
fare_structure |
A fare structure object, following the convention
set in |
fare_cutoffs |
A numeric vector. The monetary cutoffs that
should be considered when calculating the Pareto frontier. Most of the
time you'll want this parameter to be the combination of all possible
fares listed in you |
walk_speed |
A numeric. Average walk speed in km/h. Defaults to 3.6 km/h. |
bike_speed |
A numeric. Average cycling speed in km/h. Defaults to 12 km/h. |
max_rides |
An integer. The maximum number of public transport rides allowed in the same trip. Defaults to 3. |
max_lts |
An integer between 1 and 4. The maximum level of traffic stress that cyclists will tolerate. A value of 1 means cyclists will only travel through the quietest streets, while a value of 4 indicates cyclists can travel through any road. Defaults to 2. Please see details for more information. |
n_threads |
An integer. The number of threads to use when running the router in parallel. Defaults to use all available threads (Inf). |
verbose |
A logical. Whether to show |
progress |
A logical. Whether to show a progress counter when running
the router. Defaults to |
output_dir |
Either |
A data.table with the travel time and monetary cost Pareto frontier
between the specified origins and destinations. An additional column
identifying the travel time percentile is present if more than one value
was passed to percentiles. Origin and destination pairs whose trips
couldn't be completed within the maximum travel time using less money than
the specified monetary cutoffs are not returned in the data.table. If
output_dir is not NULL, the function returns the path specified in
that parameter, in which the .csv files containing the results are
saved.
R5 allows for multiple combinations of transport modes. The options
include:
Transit modes: TRAM, SUBWAY, RAIL, BUS, FERRY, CABLE_CAR,
GONDOLA, FUNICULAR. The option TRANSIT automatically considers all
public transport modes available.
Non transit modes: WALK, BICYCLE, CAR, BICYCLE_RENT,
CAR_PARK.
When cycling is enabled in R5 (by passing the value BIKE to either
mode or mode_egress), setting max_lts will allow cycling only on
streets with a given level of danger/stress. Setting max_lts to 1, for
example, will allow cycling only on separated bicycle infrastructure or
low-traffic streets and routing will revert to walking when traversing any
links with LTS exceeding 1. Setting max_lts to 3 will allow cycling on
links with LTS 1, 2 or 3. Routing also reverts to walking if the street
segment is tagged as non-bikable in OSM (e.g. a staircase), independently of
the specified max LTS.
The default methodology for assigning LTS values to network edges is based on commonly tagged attributes of OSM ways. See more info about LTS in the original documentation of R5 from Conveyal at https://docs.conveyal.com/learn-more/traffic-stress. In summary:
LTS 1: Tolerable for children. This includes low-speed, low-volume streets, as well as those with separated bicycle facilities (such as parking-protected lanes or cycle tracks).
LTS 2: Tolerable for the mainstream adult population. This includes streets where cyclists have dedicated lanes and only have to interact with traffic at formal crossing.
LTS 3: Tolerable for "enthused and confident" cyclists. This includes streets which may involve close proximity to moderate- or high-speed vehicular traffic.
LTS 4: Tolerable only for "strong and fearless" cyclists. This includes streets where cyclists are required to mix with moderate- to high-speed vehicular traffic.
For advanced users, you can provide custom LTS values by adding a tag <key = "lts"> to the osm.pbf file.
r5r ignores the timezone attribute of datetime objects when parsing dates
and times, using the study area's timezone instead. For example, let's say
you are running some calculations using Rio de Janeiro, Brazil, as your study
area. The datetime as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S") will be parsed as May 13th, 2019, 14:00h in
Rio's local time, as expected. But as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S", tz = "Europe/Paris") will also be parsed as
the exact same date and time in Rio's local time, perhaps surprisingly,
ignoring the timezone attribute.
The detailed_itineraries() and pareto_frontier() functions use an
R5-specific extension to the McRAPTOR routing algorithm. The
implementation used in detailed_itineraries() allows the router to find
paths that are optimal and less than optimal in terms of travel time, with
some heuristics around multiple access modes, riding the same patterns, etc.
The specific extension to McRAPTOR to do suboptimal path routing is not
documented yet, but a detailed description of base McRAPTOR can be found in
Delling et al (2015). The implementation used in pareto_frontier(), on the
other hand, returns only the fastest trip within a given monetary cutoff,
ignoring slower trips that cost the same. A detailed discussion on the
algorithm can be found in Conway and Stewart (2019).
Delling, D., Pajor, T., & Werneck, R. F. (2015). Round-based public transit routing. Transportation Science, 49(3), 591-604. doi:10.1287/trsc.2014.0534
Conway, M. W., & Stewart, A. F. (2019). Getting Charlie off the MTA: a multiobjective optimization method to account for cost constraints in public transit accessibility metrics. International Journal of Geographical Information Science, 33(9), 1759-1787. doi:10.1080/13658816.2019.1605075
Other routing:
detailed_itineraries(),
expanded_travel_time_matrix(),
travel_time_matrix()
library(r5r) # build transport network data_path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(data_path = data_path) # load origin/destination points points <- read.csv(file.path(data_path, "poa_hexgrid.csv"))[1:5,] # load fare structure object fare_structure_path <- system.file( "extdata/poa/fares/fares_poa.zip", package = "r5r" ) fare_structure <- read_fare_structure(fare_structure_path) departure_datetime <- as.POSIXct( "13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S" ) pf <- pareto_frontier( r5r_core, origins = points, destinations = points, mode = c("WALK", "TRANSIT"), departure_datetime = departure_datetime, fare_structure = fare_structure, fare_cutoffs = c(4.5, 4.8, 9, 9.3, 9.6) ) head(pf) stop_r5(r5r_core)library(r5r) # build transport network data_path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(data_path = data_path) # load origin/destination points points <- read.csv(file.path(data_path, "poa_hexgrid.csv"))[1:5,] # load fare structure object fare_structure_path <- system.file( "extdata/poa/fares/fares_poa.zip", package = "r5r" ) fare_structure <- read_fare_structure(fare_structure_path) departure_datetime <- as.POSIXct( "13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S" ) pf <- pareto_frontier( r5r_core, origins = points, destinations = points, mode = c("WALK", "TRANSIT"), departure_datetime = departure_datetime, fare_structure = fare_structure, fare_cutoffs = c(4.5, 4.8, 9, 9.3, 9.6) ) head(pf) stop_r5(r5r_core)
Manage cached files from the r5r package
r5r_cache(list_files = TRUE, delete_file = NULL)r5r_cache(list_files = TRUE, delete_file = NULL)
list_files |
Logical. Whether to print a message with the address of r5r
JAR files cached locally. Defaults to |
delete_file |
String. The file name (basename) of a JAR file cached
locally that should be deleted. Defaults to |
A message indicating which file exist and/or which ones have been deleted from local cache directory.
# download r5 JAR r5r::download_r5() # list all files cached r5r_cache(list_files = TRUE) # delete r5 JAR r5r_cache(delete_file = 'r5-v7.0')# download r5 JAR r5r::download_r5() # list all files cached r5r_cache(list_files = TRUE) # delete r5 JAR r5r_cache(delete_file = 'r5-v7.0')
The function reports a list with the following information:
The package version of {r5r} in use.
The installed version of R5.jar.
The Java version in use.
The amount of memory set to Java through the java.parameters option.
The user's Session Info.
r5r_sitrep()r5r_sitrep()
A list with information of the versions of the r5r package, Java
and R5 Jar in use, the memory set to Java and user's Session Info.
r5r_sitrep()r5r_sitrep()
Read a fare structure object from a file
read_fare_structure(file_path, encoding = "UTF-8")read_fare_structure(file_path, encoding = "UTF-8")
file_path |
A path pointing to a fare structure with a |
encoding |
A string. Passed to |
A fare structure object.
Other fare structure:
setup_fare_structure(),
write_fare_structure()
path <- system.file("extdata/poa/fares/fares_poa.zip", package = "r5r") fare_structure <- read_fare_structure(path)path <- system.file("extdata/poa/fares/fares_poa.zip", package = "r5r") fare_structure <- read_fare_structure(path)
Creates a basic fare structure that describes how transit fares should be
calculated in travel_time_matrix(), expanded_travel_time_matrix(),
accessibility() and pareto_frontier(). This fare structure can be
manually edited and adjusted to the existing rules in your study area, as
long as they stick to some basic premises. Please see fare structure
vignette for more information on how the fare structure works:
vignette("fare_structure", package = "r5r").
setup_fare_structure( r5r_core, base_fare, by = "MODE", debug_path = NULL, debug_info = NULL )setup_fare_structure( r5r_core, base_fare, by = "MODE", debug_path = NULL, debug_info = NULL )
r5r_core |
An object to connect with the R5 routing engine, created with
|
base_fare |
A numeric. A base value used to populate the fare structure. |
by |
A string. Describes how |
debug_path |
Either a path to a |
debug_info |
Either a string (when |
A fare structure object.
Other fare structure:
read_fare_structure(),
write_fare_structure()
library(r5r) data_path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(data_path) fare_structure <- setup_fare_structure(r5r_core, base_fare = 5) # to debug fare calculation fare_structure <- setup_fare_structure( r5r_core, base_fare = 5, debug_path = "fare_debug.csv", debug_info = "MODE" ) fare_structure$debug_settings # debugging can be manually turned off by setting output_file to "" fare_structure$debug_settings <- ""library(r5r) data_path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(data_path) fare_structure <- setup_fare_structure(r5r_core, base_fare = 5) # to debug fare calculation fare_structure <- setup_fare_structure( r5r_core, base_fare = 5, debug_path = "fare_debug.csv", debug_info = "MODE" ) fare_structure$debug_settings # debugging can be manually turned off by setting output_file to "" fare_structure$debug_settings <- ""
Builds a multimodal transport network used for routing in R5, combining
multiple data inputs present in the directory where the network should be
saved to. The directory must contain only one street network file (in
.osm.pbf format). It may optionally contain one or more public transport
GTFS feeds (in GTFS.zip format, where GTFS is the name of your feed),
when used for public transport routing, and a .tif file describing the
elevation profile of the study area. If there is more than one GTFS feed in
the directory, all feeds are merged. If there is already a 'network.dat'
file in the directory, the function will simply read it and load it to
memory (unless specified not to do so).
setup_r5( data_path, verbose = FALSE, temp_dir = FALSE, elevation = "TOBLER", overwrite = FALSE )setup_r5( data_path, verbose = FALSE, temp_dir = FALSE, elevation = "TOBLER", overwrite = FALSE )
data_path |
A string pointing to the directory where data inputs are
stored and where the built |
verbose |
A logical. Whether to show |
temp_dir |
A logical. Whether the |
elevation |
A string. The name of the impedance function to be used to
calculate impedance for walking and cycling based on street slopes.
Available options include |
overwrite |
A logical. Whether to overwrite an existing |
An rJava object to connect with R5 routing engine.
More information about the TOBLER and MINETTI options to calculate the
effects of elevation on travel times can be found in the references below:
Campbell, M. J., et al (2019). Using crowdsourced fitness tracker data to model the relationship between slope and travel rates. Applied geography, 106, 93-107. doi:10.1016/j.apgeog.2019.03.008.
Minetti, A. E., et al (2002). Energy cost of walking and running at extreme uphill and downhill slopes. Journal of applied physiology. doi:10.1152/japplphysiol.01177.2001.
Tobler, W. (1993). Three presentations on geographical analysis and modeling: Non-isotropic geographic modeling speculations on the geometry of geography global spatial analysis. Technical Report. National center for geographic information and analysis. 93 (1). https://escholarship.org/uc/item/05r820mz.
Other setup:
download_r5()
library(r5r) # directory with street network and gtfs files data_path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(data_path)library(r5r) # directory with street network and gtfs files data_path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(data_path)
Stops running r5r cores.
stop_r5(...)stop_r5(...)
... |
|
No return value, called for side effects.
Other support functions:
fileurl_from_metadata()
library(r5r) path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(path) stop_r5(r5r_core)library(r5r) path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(path) stop_r5(r5r_core)
Extract OpenStreetMap network in sf format from a network.dat file
street_network_to_sf(r5r_core)street_network_to_sf(r5r_core)
r5r_core |
An object to connect with the R5 routing engine, created with
|
A list with two components of a street network in sf format: vertices (POINT) and edges (LINESTRING).
Other network functions:
find_snap(),
transit_network_to_sf()
library(r5r) # build transport network path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(path) # extract street network from r5r_core street_net <- street_network_to_sf(r5r_core) stop_r5(r5r_core)library(r5r) # build transport network path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(path) # extract street network from r5r_core street_net <- street_network_to_sf(r5r_core) stop_r5(r5r_core)
Extracts the transit network from a network.dat file (built with
setup_r5()) in sf format.
transit_network_to_sf(r5r_core)transit_network_to_sf(r5r_core)
r5r_core |
An object to connect with the R5 routing engine, created with
|
A list with two components of a transit network in sf format:
route shapes (LINESTRING) and transit stops (POINT). The same
route_id/short_name might appear with different geometries. This occurs
when the same route is associated to more than one shape_ids in the GTFS
feed used to create the transit network. Some transit stops might be
returned with geometry POINT EMPTY (i.e. missing spatial coordinates).
This may occur when a transit stop is not snapped to the road network,
possibly because the GTFS feed used to create the transit network covers an
area larger than the .osm.pbf input data.
Other network functions:
find_snap(),
street_network_to_sf()
library(r5r) # build transport network path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(path) # extract transit network from r5r_core transit_net <- transit_network_to_sf(r5r_core) stop_r5(r5r_core)library(r5r) # build transport network path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(path) # extract transit network from r5r_core transit_net <- transit_network_to_sf(r5r_core) stop_r5(r5r_core)
Fast computation of travel time estimates between one or multiple origin destination pairs.
travel_time_matrix( r5r_core, origins, destinations, mode = "WALK", mode_egress = "WALK", departure_datetime = Sys.time(), time_window = 10L, percentiles = 50L, fare_structure = NULL, max_fare = Inf, max_walk_time = Inf, max_bike_time = Inf, max_car_time = Inf, max_trip_duration = 120L, walk_speed = 3.6, bike_speed = 12, max_rides = 3, max_lts = 2, draws_per_minute = 5L, n_threads = Inf, verbose = FALSE, progress = FALSE, output_dir = NULL )travel_time_matrix( r5r_core, origins, destinations, mode = "WALK", mode_egress = "WALK", departure_datetime = Sys.time(), time_window = 10L, percentiles = 50L, fare_structure = NULL, max_fare = Inf, max_walk_time = Inf, max_bike_time = Inf, max_car_time = Inf, max_trip_duration = 120L, walk_speed = 3.6, bike_speed = 12, max_rides = 3, max_lts = 2, draws_per_minute = 5L, n_threads = Inf, verbose = FALSE, progress = FALSE, output_dir = NULL )
r5r_core |
An object to connect with the R5 routing engine, created with
|
origins, destinations
|
Either a |
mode |
A character vector. The transport modes allowed for access,
transfer and vehicle legs of the trips. Defaults to |
mode_egress |
A character vector. The transport mode used after egress
from the last public transport. It can be either |
departure_datetime |
A POSIXct object. Please note that the departure
time only influences public transport legs. When working with public
transport networks, please check the |
time_window |
An integer. The time window in minutes for which |
percentiles |
An integer vector (max length of 5). Specifies the percentile to use when returning travel time estimates within the given time window. For example, if the 25th travel time percentile between A and B is 15 minutes, 25% of all trips taken between these points within the specified time window are shorter than 15 minutes. Defaults to 50, returning the median travel time. If a vector with length bigger than 1 is passed, the output contains an additional column for each percentile specifying the percentile travel time estimate. each estimate. Due to upstream restrictions, only 5 percentiles can be specified at a time. For more details, please see R5 documentation at https://docs.conveyal.com/analysis/methodology#accounting-for-variability. |
fare_structure |
A fare structure object, following the convention
set in |
max_fare |
A number. The maximum value that trips can cost when calculating the fastest journey between each origin and destination pair. |
max_walk_time |
An integer. The maximum walking time (in minutes) to
access and egress the transit network, to make transfers within the network
or to complete walk-only trips. Defaults to no restrictions (numeric value
of |
max_bike_time |
An integer. The maximum cycling time (in minutes) to
access and egress the transit network, to make transfers within the network
or to complete bicycle-only trips. Defaults to no restrictions (numeric
value of |
max_car_time |
An integer. The maximum driving time (in minutes) to
access and egress the transit network. Defaults to no restrictions, as long
as |
max_trip_duration |
An integer. The maximum trip duration in minutes. Defaults to 120 minutes (2 hours). |
walk_speed |
A numeric. Average walk speed in km/h. Defaults to 3.6 km/h. |
bike_speed |
A numeric. Average cycling speed in km/h. Defaults to 12 km/h. |
max_rides |
An integer. The maximum number of public transport rides allowed in the same trip. Defaults to 3. |
max_lts |
An integer between 1 and 4. The maximum level of traffic stress that cyclists will tolerate. A value of 1 means cyclists will only travel through the quietest streets, while a value of 4 indicates cyclists can travel through any road. Defaults to 2. Please see details for more information. |
draws_per_minute |
An integer. The number of Monte Carlo draws to
perform per time window minute when calculating travel time matrices and
when estimating accessibility. Defaults to 5. This would mean 300 draws in
a 60-minute time window, for example. This parameter only affects the
results when the GTFS feeds contain a |
n_threads |
An integer. The number of threads to use when running the router in parallel. Defaults to use all available threads (Inf). |
verbose |
A logical. Whether to show |
progress |
A logical. Whether to show a progress counter when running
the router. Defaults to |
output_dir |
Either |
A data.table with travel time estimates (in minutes) between
origin and destination pairs. Pairs whose trips couldn't be completed
within the maximum travel time and/or whose origin is too far from the
street network are not returned in the data.table. If output_dir is
not NULL, the function returns the path specified in that parameter, in
which the .csv files containing the results are saved.
R5 allows for multiple combinations of transport modes. The options
include:
Transit modes: TRAM, SUBWAY, RAIL, BUS, FERRY, CABLE_CAR,
GONDOLA, FUNICULAR. The option TRANSIT automatically considers all
public transport modes available.
Non transit modes: WALK, BICYCLE, CAR, BICYCLE_RENT,
CAR_PARK.
When cycling is enabled in R5 (by passing the value BIKE to either
mode or mode_egress), setting max_lts will allow cycling only on
streets with a given level of danger/stress. Setting max_lts to 1, for
example, will allow cycling only on separated bicycle infrastructure or
low-traffic streets and routing will revert to walking when traversing any
links with LTS exceeding 1. Setting max_lts to 3 will allow cycling on
links with LTS 1, 2 or 3. Routing also reverts to walking if the street
segment is tagged as non-bikable in OSM (e.g. a staircase), independently of
the specified max LTS.
The default methodology for assigning LTS values to network edges is based on commonly tagged attributes of OSM ways. See more info about LTS in the original documentation of R5 from Conveyal at https://docs.conveyal.com/learn-more/traffic-stress. In summary:
LTS 1: Tolerable for children. This includes low-speed, low-volume streets, as well as those with separated bicycle facilities (such as parking-protected lanes or cycle tracks).
LTS 2: Tolerable for the mainstream adult population. This includes streets where cyclists have dedicated lanes and only have to interact with traffic at formal crossing.
LTS 3: Tolerable for "enthused and confident" cyclists. This includes streets which may involve close proximity to moderate- or high-speed vehicular traffic.
LTS 4: Tolerable only for "strong and fearless" cyclists. This includes streets where cyclists are required to mix with moderate- to high-speed vehicular traffic.
For advanced users, you can provide custom LTS values by adding a tag <key = "lts"> to the osm.pbf file.
r5r ignores the timezone attribute of datetime objects when parsing dates
and times, using the study area's timezone instead. For example, let's say
you are running some calculations using Rio de Janeiro, Brazil, as your study
area. The datetime as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S") will be parsed as May 13th, 2019, 14:00h in
Rio's local time, as expected. But as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S", tz = "Europe/Paris") will also be parsed as
the exact same date and time in Rio's local time, perhaps surprisingly,
ignoring the timezone attribute.
The travel_time_matrix(), expanded_travel_time_matrix() and
accessibility() functions use an R5-specific extension to the RAPTOR
routing algorithm (see Conway et al., 2017). This RAPTOR extension uses a
systematic sample of one departure per minute over the time window set by the
user in the 'time_window' parameter. A detailed description of base RAPTOR
can be found in Delling et al (2015). However, whenever the user includes
transit fares inputs to these functions, they automatically switch to use an
R5-specific extension to the McRAPTOR routing algorithm.
Conway, M. W., Byrd, A., & van der Linden, M. (2017). Evidence-based transit and land use sketch planning using interactive accessibility methods on combined schedule and headway-based networks. Transportation Research Record, 2653(1), 45-53. doi:10.3141/2653-06
Delling, D., Pajor, T., & Werneck, R. F. (2015). Round-based public transit routing. Transportation Science, 49(3), 591-604. doi:10.1287/trsc.2014.0534
Other routing:
detailed_itineraries(),
expanded_travel_time_matrix(),
pareto_frontier()
library(r5r) # build transport network data_path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(data_path) # load origin/destination points points <- read.csv(file.path(data_path, "poa_points_of_interest.csv")) departure_datetime <- as.POSIXct( "13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S" ) ttm <- travel_time_matrix( r5r_core, origins = points, destinations = points, mode = c("WALK", "TRANSIT"), departure_datetime = departure_datetime, max_trip_duration = 60 ) head(ttm) # using a larger time window ttm <- travel_time_matrix( r5r_core, origins = points, destinations = points, mode = c("WALK", "TRANSIT"), departure_datetime = departure_datetime, time_window = 30, max_trip_duration = 60 ) head(ttm) # selecting different percentiles ttm <- travel_time_matrix( r5r_core, origins = points, destinations = points, mode = c("WALK", "TRANSIT"), departure_datetime = departure_datetime, time_window = 30, percentiles = c(25, 50, 75), max_trip_duration = 60 ) head(ttm) # use a fare structure and set a max fare to take monetary constraints into # account fare_structure <- read_fare_structure( file.path(data_path, "fares/fares_poa.zip") ) ttm <- travel_time_matrix( r5r_core, origins = points, destinations = points, mode = c("WALK", "TRANSIT"), departure_datetime = departure_datetime, fare_structure = fare_structure, max_fare = 5, max_trip_duration = 60, ) head(ttm) stop_r5(r5r_core)library(r5r) # build transport network data_path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(data_path) # load origin/destination points points <- read.csv(file.path(data_path, "poa_points_of_interest.csv")) departure_datetime <- as.POSIXct( "13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S" ) ttm <- travel_time_matrix( r5r_core, origins = points, destinations = points, mode = c("WALK", "TRANSIT"), departure_datetime = departure_datetime, max_trip_duration = 60 ) head(ttm) # using a larger time window ttm <- travel_time_matrix( r5r_core, origins = points, destinations = points, mode = c("WALK", "TRANSIT"), departure_datetime = departure_datetime, time_window = 30, max_trip_duration = 60 ) head(ttm) # selecting different percentiles ttm <- travel_time_matrix( r5r_core, origins = points, destinations = points, mode = c("WALK", "TRANSIT"), departure_datetime = departure_datetime, time_window = 30, percentiles = c(25, 50, 75), max_trip_duration = 60 ) head(ttm) # use a fare structure and set a max fare to take monetary constraints into # account fare_structure <- read_fare_structure( file.path(data_path, "fares/fares_poa.zip") ) ttm <- travel_time_matrix( r5r_core, origins = points, destinations = points, mode = c("WALK", "TRANSIT"), departure_datetime = departure_datetime, fare_structure = fare_structure, max_fare = 5, max_trip_duration = 60, ) head(ttm) stop_r5(r5r_core)
Writes a fare structure object do disk. Fare structure is saved as a
collection of .csv files inside a .zip file.
write_fare_structure(fare_structure, file_path)write_fare_structure(fare_structure, file_path)
fare_structure |
A fare structure object, following the convention
set in |
file_path |
A path to a |
The path passed to file_path, invisibly.
Other fare structure:
read_fare_structure(),
setup_fare_structure()
library(r5r) data_path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(data_path) fare_structure <- setup_fare_structure(r5r_core, base_fare = 5) tmpfile <- tempfile("sample_fare_structure", fileext = ".zip") write_fare_structure(fare_structure, tmpfile)library(r5r) data_path <- system.file("extdata/poa", package = "r5r") r5r_core <- setup_r5(data_path) fare_structure <- setup_fare_structure(r5r_core, base_fare = 5) tmpfile <- tempfile("sample_fare_structure", fileext = ".zip") write_fare_structure(fare_structure, tmpfile)